ObliVM: A Programming Framework for Secure Computation

168

115

Abstract:We propose privacy-preserving protocols for computing linear regression models, in the setting where the training dataset is vertically distributed among several parties. Our main contribution is a hybrid multi-party computation protocol that combines Yao's garbled circuits with tailored protocols for computing inner products. Like many machine learning tasks, building a linear regression model involves solving a system of linear equations. We conduct a comprehensive evaluation and comparison of different techniques for securely performing this task, including a new Conjugate Gradient Descent (CGD) algorithm. This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Our technique improves on Nikolaenko et al.'s method for privacy-preserving ridge regression (S&P 2013), and can be used as a building block in other analyses. We implement a complete system and demonstrate that our approach is highly scalable, solving data analysis problems with one million records and one hundred features in less than one hour of total running time.

Section: Multi-party Computationmentioning

confidence: 99%

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

Gascón

Schoppmann

Balle

et al. 2017

168

115

“…[19] introduces an outsourced secure computation scheme that is secure against active adversaries and uses it to compute Dijkstra's shortest path algorithm. [20] introduces a framework that compiles high-level descriptions into programs that combine secure computation and ORAM and gives speed-ups for Dijkstra's shortest path algorithm. However, the complexities of these algorithms that hide the topology of the graph are too high to scale to the size of the Internet consisting of thousands of nodes.…”

Section: [3] and Other Related Workmentioning

confidence: 99%

“…The aforementioned constructions of MPC are purely theoretical, and protocols for secure computation can require many rounds of interaction and the transformation of massive data between the computing parties. A very productive line of research, e.g., [6,20,[22][23][24][25], has been devoted to positioning MPC as a practical tool and off-the-shelf solution for a wide variety of problems, and to minimize the complexity of the current schemes. Using these recent breakthroughs, the benefits of MPC can be utilized in some real-life applications [26,27].…”

Section: Secure Multi-party Computationmentioning

confidence: 99%

“…Furthermore, we estimate that the total number of gates is around 400 million. Since modern secure computation frameworks are able to evaluate 4-8 million AND gates per second [6,20], securely evaluating the circuit would require nearly 100 s, which is arguably too long. Finally, the high depth translates to a huge number of communication rounds for the GMW protocol.…”

Section: If-condition (Mux)mentioning

confidence: 99%

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Privacy-Preserving Interdomain Routing at Internet Scale

Asharov

Demmler

Schapira

et al. 2017

Abstract:The Border Gateway Protocol (BGP) computes routes between the organizational networks that make up today's Internet. Unfortunately, BGP suffers from deficiencies, including slow convergence, security problems, a lack of innovation, and the leakage of sensitive information about domains' routing preferences. To overcome some of these problems, we revisit the idea of centralizing and using secure multi-party computation (MPC) for interdomain routing which was proposed by Gupta et al. (ACM HotNets'12). We implement two algorithms for interdomain routing with state-of-the-art MPC protocols. On an empirically derived dataset that approximates the topology of today's Internet (55 809 nodes), our protocols take as little as 6 s of topologyindependent precomputation and only 3 s of online time. We show, moreover, that when our MPC approach is applied at country/region-level scale, runtimes can be as low as 0.17 s online time and 0.20 s pre-computation time. Our results motivate the MPC approach for interdomain routing and furthermore demonstrate that current MPC techniques are capable of efficiently tackling real-world problems at a large scale.

“…In the last few years, researchers have tried to address these needs by proposing different privacy-preserving solutions that enable several data providers to securely store and share their sensitive data in either a centralized or decentralized way [3,11,14,26,28,30,33,39,40,42,49,50]. Yet, despite the acknowledgment and acceptance that most of these solutions have received in the research community of privacy and security, only a few have been converted into concrete operational tools and deployed in the real world [3,14,39].…”

mentioning

confidence: 99%

UnLynx: A Decentralized System for Privacy-Conscious Data Sharing

Froelicher

Egger

Sousa

et al. 2017

Current solutions for privacy-preserving data sharing among multiple parties either depend on a centralized authority that must be trusted and provides only weakest-link security (e.g., the entity that manages private/secret cryptographic keys), or leverage on decentralized but impractical approaches (e.g., secure multi-party computation). When the data to be shared are of a sensitive nature and the number of data providers is high, these solutions are not appropriate. Therefore, we present UnLynx, a new decentralized system for efficient privacypreserving data sharing. We consider m servers that constitute a collective authority whose goal is to verifiably compute on data sent from n data providers. UnLynx guarantees the confidentiality, unlinkability between data providers and their data, privacy of the end result and the correctness of computations by the servers. Furthermore, to support differentially private queries, UnLynx can collectively add noise under encryption. All of this is achieved through a combination of a set of new distributed and secure protocols that are based on homomorphic cryptography, verifiable shuffling and zero-knowledge proofs. UnLynx is highly parallelizable and modular by design as it enables multiple security/privacy vs. runtime tradeoffs. Our evaluation shows that UnLynx can execute a secure survey on 400,000 personal data records containing 5 encrypted attributes, distributed over 20 independent databases, for a total of 2,000,000 ciphertexts, in 24 minutes.